research
∙
07/12/2023
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
Dividing ads ranking system into retrieval, early, and final stages is a...
share
research
∙
06/26/2023
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
This work extends the theory of identifiability in supervised learning b...
research
∙
05/25/2023
Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning
We examine the characteristic activation values of individual ReLU units...
research
∙
05/05/2022
Meta-learning Feature Representations for Adaptive Gaussian Processes via Implicit Differentiation
We propose Adaptive Deep Kernel Fitting (ADKF), a general framework for ...
research
∙
08/26/2021
Exact Decoding Probability of Sparse Random Linear Network Coding for Reliable Multicast
Sparse random linear network coding (SRLNC) used as a class of erasure c...
research
∙
10/26/2020
Optimal Client Sampling for Federated Learning
It is well understood that client-master communication can be a primary ...
research
∙
10/12/2020
The Decoding Success Probability of Sparse Random Linear Network Coding for Multicast
Reliable and low latency multicast communication is important for future...
research
∙
05/31/2019
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation mo...
research
∙
03/22/2016
Multi-Scale Convolutional Neural Networks for Time Series Classification
Time series classification (TSC), the problem of predicting class labels...
research
∙
06/14/2015
Compressing Convolutional Neural Networks
Convolutional neural networks (CNN) are increasingly used in many areas ...
research
∙
04/19/2015